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Ransomware is an increasingly prevalent cybersecurity hazard due to its ability to encrypt data and request payment for its decryption. The threat's dynamic nature generally renders conventional ransomware detection methods ineffective. This paper suggests an innovative method for detecting ransomware that capitalizes on artificial intelligence (AI) and machine learning (ML). A novel technique has been developed that integrates robust anomaly detection and classification algorithms with advanced feature extraction from system logs, network traffic, and file metadata. This technique achieves high accuracy with minimal false-positive rates by employing autoencoders, isolated forests for anomaly detection, random forests, and support vector machines for classification. The method's ability to substantially improve ransomware defenses has been demonstrated through extensive testing on a large dataset, revealing that it outperforms current approaches. The study establishes a firm foundation for proactive ransomware detection and mitigation by demonstrating the advantages of integrating AI and ML in cybersecurity. [ABSTRACT FROM AUTHOR]

The use of counterfeit integrated circuits (ICs) in electronic products decreases its quality and lifetime. Recycled ICs can be detected by the method of aging analysis. Aging is carried out through reliability analysis with the effect of hot carrier injection and bias temperature instability (BTI). In this work, three machine learning methods, namely K‐means clustering, back propagation neural network (BPNN) and support vector machines (SVMs), are used to detect the recycled IC aged for a shorter period (1 day) with minimum data size. This work also distinguishes the effects of degradation due to process variations and reliability effects. The reliability and Monte Carlo simulation are performed on benchmark circuits such as c17, s27, b02 and fully differential folded‐cascode amplifier using the Cadence Virtuoso tool, and the parameters such as minimum voltage, delay value, supply current, gain, phase margin and bandwidth are measured. Machine learning methods are developed using MATLAB to train and classify the parameters. From the results obtained, it is observed that the classification rate for the benchmark circuits is 100%, and using BPNN, K‐means clustering and SVM and the proposed method, recycled IC or used IC is detected even if it was used for 1 day. [ABSTRACT FROM AUTHOR]